Finally, we compare the performance of the proposed TEDGE caching framework with five state-of-the-art caching schemes on the Movielens dataset, including LRU, LFU, PopCaching [1]}, LSTM-C [2]}, and the TRansformer (TR) caching, which is an upgraded version of the attention-based neural network in Reference [3]}. While the attention-based model in Reference [3]} is used for predicting the request pattern of online content, we adopt it to predict the Top-\(K\)  popular ones. Fig. REF  compares the performance of the proposed TEDGE scheme with other baselines mentioned above from the aspect of the cache-hit ratio, when the DNN models reach the steady state. In the content caching context, cache-hit-ratio is a widely used metric, illustrating the ratio of requests served by hgNBs versus total requests. Considering the Zipf distribution for the content popularity profile, we set the storage capacity of hgNBs to \(10\%\)  of the total content [5]}. As shown in Fig. REF , the optimal strategy [2]} is a caching scheme, where all requests are served through hgNBs, which cannot be obtained in reality. According to the results in Fig. REF , the proposed TEDGE caching framework obtains the highest cache-hit-ratio in comparison to its state-of-the-art counterparts.
